Lee, W.; Lee, J. Tree-Based Modeling for Large-Scale Management in Agriculture: Explaining Organic Matter Content in Soil. Appl. Sci.2024, 14, 1811.
Lee, W.; Lee, J. Tree-Based Modeling for Large-Scale Management in Agriculture: Explaining Organic Matter Content in Soil. Appl. Sci. 2024, 14, 1811.
Lee, W.; Lee, J. Tree-Based Modeling for Large-Scale Management in Agriculture: Explaining Organic Matter Content in Soil. Appl. Sci.2024, 14, 1811.
Lee, W.; Lee, J. Tree-Based Modeling for Large-Scale Management in Agriculture: Explaining Organic Matter Content in Soil. Appl. Sci. 2024, 14, 1811.
Abstract
Machine learning has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of machine learning models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9,584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg-1, which favor enhanced organic matter in the soil. Different models identified different impacts of nutrients on the organic matter level in the soil. The SHAP method is practical for assessing whether different machine learning models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances.
Keywords
agricultural data analysis; agricultural business management; tree-based models; SHAP; soil organic matter; economic analysis
Subject
Environmental and Earth Sciences, Soil Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
